StatisticsRelevanceTable#

class StatisticsRelevanceTable[source]#

Bases: RelevanceTable

StatisticsRelevanceTable builds feature relevance table with tsfresh statistics.

Init RelevanceTable.

Parameters:: greater_is_better – bool flag, if True the biggest value in relevance table corresponds to the most important exog feature

__call__(df: DataFrame, df_exog: DataFrame, return_ranks: bool = False, **kwargs) → DataFrame[source]#

Compute feature relevance table with get_statistics_relevance_table() method.

Parameters:

df (DataFrame) –
df_exog (DataFrame) –
return_ranks (bool) –

Return type:

DataFrame

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:: **params (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )

to_dict()[source]#: Collect all information about etna object in dict.